A five-agent workflow to systematically identify divergent protein-membrane-interface sites across class A GPCRs and exploit them for selective allosteric ligand discovery is developed, establishing a scalable strategy for translating GPCR membrane-interface divergence into precise allosteric sites and testable subtype-selective ligand candidates.
Abstract
Closely related G protein-coupled receptor (GPCR) subtypes often share highly conserved orthosteric pockets, making subtype-selective ligand development challenging. Here, we developed a five-agent workflow to systematically identify divergent protein-membrane-interface sites across class A GPCRs and exploit them for selective allosteric ligand discovery. By combining dMaSIF-derived surface fingerprints with Ballesteros-Weinstein (BW) position alignment, we compared structurally equivalent membrane-facing regions and identified the three most divergent hotspots for each of 163 receptor pairs. These regions showed substantial spatial overlap with experimentally characterized allosteric sites. Paired target-off-target screening of one million lead-like compounds, followed by detail-mode redocking and multi-seed consistency filtering, yielded 352 receptor-pair-specific candidates corresponding to 344 unique compounds across 104 receptor pairs. These candidates, together with their divergent sites and predicted selectivity profiles, were integrated into a searchable database. Our findings establish a scalable strategy for translating GPCR membrane-interface divergence into precise allosteric sites and testable subtype-selective ligand candidates.
GaMD ensemble docking improved early AM enrichment across all four targets under at least one program, and the Boltz-2 deep-learning program showed minimal sensitivity to GaMD templates and underperformed conventional docking, suggesting its affinity predictions complement rather than replace physics- and empirical-based docking approaches for GPCR AM screening.
T.D. Thompson, Yinglong Miao· bioRxiv· 0 citations
This study provides potential lead compounds for the design of small-molecule allosteric drugs targeting class B1 GPCRs and performs conformational sampling and combined dynamic pocket detection algorithms, MDpocket and FTMove, to identify six characteristic cryptic pockets within the dynamic trajectories.
Zhi Dong, Long Cheng, Qingxin Shi et al.· International Journal of Bio...· 0 citations
BACKGROUND AND PURPOSE
G protein-coupled receptors (GPCRs) are integral membrane proteins that mediate physiological processes by enabling cells to detect and respond to diverse stimuli. Although many subfamily-specific functional hotspots have been described, the family-wide determinants of common and subfamily-specific functions remain incomplete.
EXPERIMENTAL APPROACH
Here, we developed an evolutionary framework utilizing conservation within orthologs and variation across paralogs to classify positions as common residues (CRs) or selective residues (SRs).
KEY RESULTS
Common residues (CRs) cluster in sites linked to structural stability and activation, whereas SRs concentrate at selective interfaces involved in ligand and transducer binding. SR distributions across families revealed that some classes mainly diversify through changes in ligand-recognition sites, whereas others through changes at transducer-binding interfaces. We also uncovered CRs involved in family-specific and cross-family motifs, including conserved disulfide bridges and cholesterol-contact sites.
CONCLUSIONS AND IMPLICATIONS
Together, these findings provide an evolutionary blueprint of family-wide features, reinforce known associations and deliver testable hypotheses that are especially valuable for understudied families.
Berkay Selçuk, Gunnar Schulte, I. Zhulin et al.· British Journal of Pharmacol...· 0 citations
Understanding how allosteric modulators influence protein dynamics is essential for guiding drug design. This work analyses a total of 45 μs of classical molecular dynamics simulations for four class A G-protein-coupled receptors (GPCRs), namely the Complement C5a receptor (C5AR1), the Purinergic Receptor P2Y (P2RY1), and the Cannabinoid Receptors 1 and 2 (CNR1/CNR2). Protein dynamics is essential to detect the shallow extrahelical binding sites, such as the one found in P2RY1. Current methods for computing Allosteric Communication Networks (ACNs) produce complex outputs requiring expert interpretation. To address this, we focus on the shortest paths of information transfer between the orthosteric and G-protein binding sites in Class A GPCRs. Our retrospective analysis reveals state- and bias ligand-dependent residue interactions along these communication pathways. Furthermore, focusing on the predicted binding site of allosteric modulator EC21a at cannabinoid receptors, the ACN framework was used to prioritize two residues for mutational analysis that may contribute to allosteric communication.
S. Peter, G. Chalhoub, Peter J. McCormick et al.· Journal of Chemical Informat...· 0 citations
This Primer outlines experimental and computational workflows tailored to peptide–GPCR interactions, including in silico peptide mining, deorphanization strategies, library-based screening platforms, modern pathway-resolved biosensor assays, and approaches for peptide stabilization and optimization strategies to address their pharmacokinetic limitations.
J. Hermes, Marin Matic, H. Yeung et al.· Nature Reviews Methods Prime...· 0 citations
G protein-coupled receptors (GPCRs) are therapeutic targets for over 30% of approved drugs, yet specific GPCR subtypes act as molecular initiating events in several neurotoxic adverse outcome pathways. Therefore, knowledge of GPCR-ligand interactions is critical for drug discovery and computational toxicology. However, accurate predictions of GPCR-ligand binding can be challenging due to receptor conformational flexibility, complex membrane environment, and lack of selectivity among ligands. Drug-target interaction (DTI) models that jointly encode protein and ligand representations offer a promising approach to predict these interactions. In this study, we identified 119 neurologically relevant GPCRs and evaluated three deep learning architectures for creating a unified GPCR-ligand DTI model: dual-projection cosine similarity networks and transformer encoders (both using pre-computed embeddings) as well as bidirectional cross-attention networks (with frozen or fine-tuned encoders). Unlike prior studies that rely primarily on random splits, we evaluated model performance across random-split, cluster-split, and novel protein scenarios to provide realistic estimates of generalization. All the models performed well, achieving area under the receiver operating characteristic curve (AUROC) values of 0.91 on random-split validation, 0.81 on cluster-split testing (structurally distinct ligands), and 0.64 on novel GPCR generalization (proteins unseen during training). However, in the unseen-proteins test set, predictions were less accurate for GPCRs from protein families not represented in the training data or those with contrasting ligand interactions. We also assessed the models' ability to detect ligand selectivity, achieving an AUROC of 0.71 on ligands with at least five known GPCR interactions, although sensitivity remained low at 0.49. Our curated neurological GPCR dataset and rigorous evaluation framework provide realistic model assessment, reveal where current models succeed or fail, and provide practical guidance for deploying GPCR-ligand predictors in drug discovery and toxicity screening.Scientific contributionOur work connects computational toxicology and GPCR pharmacology by systematically evaluating transformers and similarity-based proteochemometric architectures for predicting CNS-relevant GPCR-ligand interactions under rigorous cold-start conditions. Our analysis reveals that while models achieve strong performance on structurally novel ligands, generalization to novel GPCRs remains challenging, with particularly poor prediction for out-of-family receptors. These results provide a rigorous evaluation framework for predicting ligand interactions against neurologically relevant GPCRs and highlight the need for improved protein representation learning in computational toxicology frameworks.
Souvik Dey, Pinyi Lu, Anders Wallqvist et al.· Journal of Cheminformatics· 0 citations